






"""simple docstring"""


import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	List[Any]=13     , UpperCAmelCase							:	Optional[Any]=7     , UpperCAmelCase							:	int=True     , UpperCAmelCase							:	Dict=True     , UpperCAmelCase							:	Optional[int]=False     , UpperCAmelCase							:	Union[str, Any]=True     , UpperCAmelCase							:	Tuple=99     , UpperCAmelCase							:	Optional[int]=32     , UpperCAmelCase							:	str=5     , UpperCAmelCase							:	Union[str, Any]=4     , UpperCAmelCase							:	int=37     , UpperCAmelCase							:	Union[str, Any]="gelu"     , UpperCAmelCase							:	Tuple=0.1     , UpperCAmelCase							:	str=0.1     , UpperCAmelCase							:	Any=512     , UpperCAmelCase							:	str=16     , UpperCAmelCase							:	Optional[Any]=2     , UpperCAmelCase							:	List[str]=0.0_2     , UpperCAmelCase							:	int=3     , UpperCAmelCase							:	Optional[int]=4     , UpperCAmelCase							:	Optional[int]=None     , )	->       Union[str, Any]:
											lowerCAmelCase   :Tuple						=			parent
											lowerCAmelCase   :int						=			batch_size
											lowerCAmelCase   :Tuple						=			seq_length
											lowerCAmelCase   :str						=			is_training
											lowerCAmelCase   :Dict						=			use_input_mask
											lowerCAmelCase   :Tuple						=			use_token_type_ids
											lowerCAmelCase   :Optional[int]						=			use_labels
											lowerCAmelCase   :Optional[Any]						=			vocab_size
											lowerCAmelCase   :Optional[int]						=			hidden_size
											lowerCAmelCase   :Any						=			num_hidden_layers
											lowerCAmelCase   :List[Any]						=			num_attention_heads
											lowerCAmelCase   :Union[str, Any]						=			intermediate_size
											lowerCAmelCase   :Dict						=			hidden_act
											lowerCAmelCase   :Optional[int]						=			hidden_dropout_prob
											lowerCAmelCase   :Tuple						=			attention_probs_dropout_prob
											lowerCAmelCase   :Tuple						=			max_position_embeddings
											lowerCAmelCase   :Union[str, Any]						=			type_vocab_size
											lowerCAmelCase   :int						=			type_sequence_label_size
											lowerCAmelCase   :Optional[Any]						=			initializer_range
											lowerCAmelCase   :int						=			num_labels
											lowerCAmelCase   :str						=			num_choices
											lowerCAmelCase   :List[str]						=			scope
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       int:
											lowerCAmelCase   :Union[str, Any]						=			ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size   )

											lowerCAmelCase   :str						=			None
											if self.use_input_mask:
																	lowerCAmelCase   :Dict						=			random_attention_mask([self.batch_size, self.seq_length]   )

											lowerCAmelCase   :int						=			None
											if self.use_token_type_ids:
																	lowerCAmelCase   :Tuple						=			ids_tensor([self.batch_size, self.seq_length]     , self.type_vocab_size   )

											lowerCAmelCase   :List[str]						=			None
											lowerCAmelCase   :Optional[Any]						=			None
											lowerCAmelCase   :Optional[int]						=			None
											if self.use_labels:
																	lowerCAmelCase   :str						=			ids_tensor([self.batch_size]     , self.type_sequence_label_size   )
																	lowerCAmelCase   :Any						=			ids_tensor([self.batch_size, self.seq_length]     , self.num_labels   )
																	lowerCAmelCase   :Any						=			ids_tensor([self.batch_size]     , self.num_choices   )

											lowerCAmelCase   :Optional[Any]						=			self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[Any]:
											return LlamaConfig(
											    vocab_size=self.vocab_size     , hidden_size=self.hidden_size     , num_hidden_layers=self.num_hidden_layers     , num_attention_heads=self.num_attention_heads     , intermediate_size=self.intermediate_size     , hidden_act=self.hidden_act     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , max_position_embeddings=self.max_position_embeddings     , type_vocab_size=self.type_vocab_size     , is_decoder=UpperCAmelCase     , initializer_range=self.initializer_range     , )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	int     , UpperCAmelCase							:	str     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	int     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	str   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			LlamaModel(config=UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Union[str, Any]						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			model(UpperCAmelCase   )
											self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	int     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Tuple     , )	->       int:
											lowerCAmelCase   :Any						=			True
											lowerCAmelCase   :Optional[Any]						=			LlamaModel(UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Optional[int]						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=UpperCAmelCase     , )
											lowerCAmelCase   :Union[str, Any]						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , )
											lowerCAmelCase   :Union[str, Any]						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase   )
											self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Optional[Any]     , )	->       List[Any]:
											lowerCAmelCase   :List[Any]						=			LlamaForCausalLM(config=UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Tuple						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase     , labels=UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	Dict     , )	->       Optional[Any]:
											lowerCAmelCase   :str						=			True
											lowerCAmelCase   :List[Any]						=			True
											lowerCAmelCase   :Union[str, Any]						=			LlamaForCausalLM(config=UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()

											# first forward pass
											lowerCAmelCase   :int						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=UpperCAmelCase     , use_cache=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			outputs.past_key_values

											# create hypothetical multiple next token and extent to next_input_ids
											lowerCAmelCase   :Any						=			ids_tensor((self.batch_size, 3)     , config.vocab_size   )
											lowerCAmelCase   :Dict						=			ids_tensor((self.batch_size, 3)     , vocab_size=2   )

											# append to next input_ids and
											lowerCAmelCase   :List[Any]						=			torch.cat([input_ids, next_tokens]     , dim=-1   )
											lowerCAmelCase   :Union[str, Any]						=			torch.cat([input_mask, next_mask]     , dim=-1   )

											lowerCAmelCase   :Dict						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=UpperCAmelCase     , output_hidden_states=UpperCAmelCase     , )['hidden_states'][0]
											lowerCAmelCase   :Optional[Any]						=			model(
											    UpperCAmelCase     , attention_mask=UpperCAmelCase     , encoder_hidden_states=UpperCAmelCase     , encoder_attention_mask=UpperCAmelCase     , past_key_values=UpperCAmelCase     , output_hidden_states=UpperCAmelCase     , )['hidden_states'][0]

											# select random slice
											lowerCAmelCase   :Optional[Any]						=			ids_tensor((1,)     , output_from_past.shape[-1]   ).item()
											lowerCAmelCase   :Tuple						=			output_from_no_past[:, -3:, random_slice_idx].detach()
											lowerCAmelCase   :Tuple						=			output_from_past[:, :, random_slice_idx].detach()

											self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(UpperCAmelCase     , UpperCAmelCase     , atol=1e-3   )   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Union[str, Any]:
											lowerCAmelCase   :Optional[Any]						=			self.prepare_config_and_inputs()
											(
											    (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, (
											    lowerCAmelCase
											)	, 
											)   :Any						=			config_and_inputs
											lowerCAmelCase   :List[Any]						=			{'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict




@require_torch
class 						__UpperCamelCase     (     UpperCamelCase   ,     UpperCamelCase   ,     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				List[Any]      =  (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
					lowercase_    :				Tuple      =  (LlamaForCausalLM,) if is_torch_available() else ()
					lowercase_    :				List[str]      =  (
					    {
					        """feature-extraction""": LlamaModel,
					        """text-classification""": LlamaForSequenceClassification,
					        """text-generation""": LlamaForCausalLM,
					        """zero-shot""": LlamaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowercase_    :				Union[str, Any]      =  False
					lowercase_    :				Any      =  False
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Dict:
											lowerCAmelCase   :Optional[int]						=			LlamaModelTester(self   )
											lowerCAmelCase   :Optional[Any]						=			ConfigTester(self     , config_class=UpperCAmelCase     , hidden_size=37   )
					def        UpperCAmelCase__ (							self							:	str   )	->       Optional[Any]:
											self.config_tester.run_common_tests()
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[Any]:
											lowerCAmelCase   :Tuple						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Dict:
											lowerCAmelCase   :Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowerCAmelCase   :int						=			type
																	self.model_tester.create_and_check_model(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       List[str]:
											lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
											lowerCAmelCase   :Optional[int]						=			3
											lowerCAmelCase   :Optional[Any]						=			input_dict['input_ids']
											lowerCAmelCase   :int						=			input_ids.ne(1   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			ids_tensor([self.model_tester.batch_size]     , self.model_tester.type_sequence_label_size   )
											lowerCAmelCase   :int						=			LlamaForSequenceClassification(UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Tuple						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase     , labels=UpperCAmelCase   )
											self.assertEqual(result.logits.shape     , (self.model_tester.batch_size, self.model_tester.num_labels)   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       List[Any]:
											lowerCAmelCase	, lowerCAmelCase   :str						=			self.model_tester.prepare_config_and_inputs_for_common()
											lowerCAmelCase   :List[str]						=			3
											lowerCAmelCase   :Dict						=			'single_label_classification'
											lowerCAmelCase   :List[str]						=			input_dict['input_ids']
											lowerCAmelCase   :List[str]						=			input_ids.ne(1   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Union[str, Any]						=			ids_tensor([self.model_tester.batch_size]     , self.model_tester.type_sequence_label_size   )
											lowerCAmelCase   :List[Any]						=			LlamaForSequenceClassification(UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :int						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase     , labels=UpperCAmelCase   )
											self.assertEqual(result.logits.shape     , (self.model_tester.batch_size, self.model_tester.num_labels)   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Union[str, Any]:
											lowerCAmelCase	, lowerCAmelCase   :str						=			self.model_tester.prepare_config_and_inputs_for_common()
											lowerCAmelCase   :int						=			3
											lowerCAmelCase   :int						=			'multi_label_classification'
											lowerCAmelCase   :str						=			input_dict['input_ids']
											lowerCAmelCase   :Union[str, Any]						=			input_ids.ne(1   ).to(UpperCAmelCase   )
											lowerCAmelCase   :int						=			ids_tensor(
											    [self.model_tester.batch_size, config.num_labels]     , self.model_tester.type_sequence_label_size   ).to(torch.float   )
											lowerCAmelCase   :List[str]						=			LlamaForSequenceClassification(UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Dict						=			model(UpperCAmelCase     , attention_mask=UpperCAmelCase     , labels=UpperCAmelCase   )
											self.assertEqual(result.logits.shape     , (self.model_tester.batch_size, self.model_tester.num_labels)   )
					@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Tuple:
											pass
					@parameterized.expand([('linear',), ('dynamic',)]   )
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	int   )	->       int:
											lowerCAmelCase	, lowerCAmelCase   :int						=			self.model_tester.prepare_config_and_inputs_for_common()
											lowerCAmelCase   :List[Any]						=			ids_tensor([1, 10]     , config.vocab_size   )
											lowerCAmelCase   :Union[str, Any]						=			ids_tensor([1, int(config.max_position_embeddings * 1.5   )]     , config.vocab_size   )

											set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
											lowerCAmelCase   :Tuple						=			LlamaModel(UpperCAmelCase   )
											original_model.to(UpperCAmelCase   )
											original_model.eval()
											lowerCAmelCase   :Union[str, Any]						=			original_model(UpperCAmelCase   ).last_hidden_state
											lowerCAmelCase   :Tuple						=			original_model(UpperCAmelCase   ).last_hidden_state

											set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
											lowerCAmelCase   :int						=			{'type': scaling_type, 'factor': 1_0.0}
											lowerCAmelCase   :Tuple						=			LlamaModel(UpperCAmelCase   )
											scaled_model.to(UpperCAmelCase   )
											scaled_model.eval()
											lowerCAmelCase   :Optional[Any]						=			scaled_model(UpperCAmelCase   ).last_hidden_state
											lowerCAmelCase   :str						=			scaled_model(UpperCAmelCase   ).last_hidden_state

											# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
											# maximum sequence length, so the outputs for the short input should match.
											if scaling_type == "dynamic":
																	self.assertTrue(torch.allclose(UpperCAmelCase     , UpperCAmelCase     , atol=1e-5   )   )
											else:
																	self.assertFalse(torch.allclose(UpperCAmelCase     , UpperCAmelCase     , atol=1e-5   )   )

											# The output should be different for long inputs
											self.assertFalse(torch.allclose(UpperCAmelCase     , UpperCAmelCase     , atol=1e-5   )   )




@require_torch
class 						__UpperCamelCase     (     unittest.TestCase						):
					@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
					@slow
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Tuple:
											lowerCAmelCase   :str						=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
											lowerCAmelCase   :Union[str, Any]						=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'     , device_map='auto'   )
											lowerCAmelCase   :List[str]						=			model(torch.tensor([input_ids]   )   )
											# Expected mean on dim = -1
											lowerCAmelCase   :str						=			torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]   )
											torch.testing.assert_close(out.mean(-1   )     , UpperCAmelCase     , atol=1e-2     , rtol=1e-2   )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											lowerCAmelCase   :int						=			torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]   )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]     , UpperCAmelCase     , atol=1e-5     , rtol=1e-5   )
					@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
					@slow
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       int:
											lowerCAmelCase   :Dict						=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
											lowerCAmelCase   :str						=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'     , device_map='auto'   )
											lowerCAmelCase   :Tuple						=			model(torch.tensor(UpperCAmelCase   )   )
											# Expected mean on dim = -1
											lowerCAmelCase   :Dict						=			torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]   )
											torch.testing.assert_close(out.mean(-1   )     , UpperCAmelCase     , atol=1e-2     , rtol=1e-2   )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											lowerCAmelCase   :str						=			torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]   )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]     , UpperCAmelCase     , atol=1e-5     , rtol=1e-5   )
					@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
					@slow
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       List[Any]:
											lowerCAmelCase   :List[Any]						=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
											lowerCAmelCase   :Union[str, Any]						=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'     , device_map='auto'   )
											lowerCAmelCase   :Dict						=			model(torch.tensor(UpperCAmelCase   )   )
											# Expected mean on dim = -1
											lowerCAmelCase   :int						=			torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]   )
											torch.testing.assert_close(out.mean(-1   )     , UpperCAmelCase     , atol=1e-2     , rtol=1e-2   )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											lowerCAmelCase   :Dict						=			torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]   )
											# fmt: on
											torch.testing.assert_close(out.mean(-1   )     , UpperCAmelCase     , atol=1e-2     , rtol=1e-2   )
					@unittest.skip(
					    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'   )
					@slow
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[Any]:
											lowerCAmelCase   :Optional[Any]						=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
											lowerCAmelCase   :List[str]						=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'     , device_map='auto'   )
											lowerCAmelCase   :List[str]						=			model(torch.tensor(UpperCAmelCase   )   )

											lowerCAmelCase   :int						=			torch.tensor(
											    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]     , dtype=torch.floataa   )
											torch.testing.assert_close(out.mean(-1   )     , UpperCAmelCase     , atol=1e-2     , rtol=1e-2   )
											# fmt: off
											lowerCAmelCase   :Optional[int]						=			torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]   )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]     , UpperCAmelCase     , atol=1e-5     , rtol=1e-5   )
					@unittest.skip('Model is curently gated'   )
					@slow
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Optional[int]:
											lowerCAmelCase   :Union[str, Any]						=			'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
											lowerCAmelCase   :Any						=			'Simply put, the theory of relativity states that '
											lowerCAmelCase   :List[str]						=			LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'   )
											lowerCAmelCase   :str						=			tokenizer.encode(UpperCAmelCase     , return_tensors='pt'   )
											lowerCAmelCase   :int						=			LlamaForCausalLM.from_pretrained(
											    'meta-llama/Llama-2-13b-chat-hf'     , device_map='sequential'     , use_safetensors=UpperCAmelCase   )

											# greedy generation outputs
											lowerCAmelCase   :Optional[int]						=			model.generate(UpperCAmelCase     , max_new_tokens=64     , top_p=UpperCAmelCase     , temperature=1     , do_sample=UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			tokenizer.decode(generated_ids[0]     , skip_special_tokens=UpperCAmelCase   )
											self.assertEqual(UpperCAmelCase     , UpperCAmelCase   )







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE   =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AdaptiveEmbedding',
		    'TransfoXLForSequenceClassification',
		    'TransfoXLLMHeadModel',
		    'TransfoXLModel',
		    'TransfoXLPreTrainedModel',
		    'load_tf_weights_in_transfo_xl',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAdaptiveEmbedding',
		    'TFTransfoXLForSequenceClassification',
		    'TFTransfoXLLMHeadModel',
		    'TFTransfoXLMainLayer',
		    'TFTransfoXLModel',
		    'TFTransfoXLPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
		from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_transfo_xl import (
				    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AdaptiveEmbedding,
				    TransfoXLForSequenceClassification,
				    TransfoXLLMHeadModel,
				    TransfoXLModel,
				    TransfoXLPreTrainedModel,
				    load_tf_weights_in_transfo_xl,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_transfo_xl import (
				    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAdaptiveEmbedding,
				    TFTransfoXLForSequenceClassification,
				    TFTransfoXLLMHeadModel,
				    TFTransfoXLMainLayer,
				    TFTransfoXLModel,
				    TFTransfoXLPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)